A good understanding of data structuring is key for anyone who has to deal with large amounts of data. Data structuring is the process of arranging data to allow it to be used and accessed by software programs. There are a myriad of ways of structuring data, but the most common ones are linked lists, arrays and dictionary. Each has its own strengths and weaknesses, so it is crucial to comprehend each one is best suited to your specific requirements.
Linear Data Structures
Linear data structures differ based on the programming languages they are written in. They can differ in their allocation of memory as well as internal structure and the way basic operations such as traversal, retrieval, insertion, and deletion are performed. One good example of a linear data structure is an array, which has its elements stored in multiple memory locations that are contiguous, where each element can be accessed by using an index that starts at 0. These are good for algorithms that require sequential data access. They are also useful for storing lists (like dates or addresses) and performing math computations.
Non-Linear Data Structures
A tree is an innovative document management unlinear structure that arranges data in a hierarchy. Each record (or node) in a tree is linked to one or more successor records through a parent/child connection. Each child record could contain information or pointers to other records in the tree. A family tree is an example of one. The primary data is at the top and it branches out to children, grandchildren, etc.
